Comparative analysis of rural and urban market prices for garri in Edo State, Nigeria: Implications for food security
Bibliographic record
Abstract
This study compared the prices of garri in both rural and urban markets of Edo State, Nigeria covering the period 1990 to 2005. Specifically, the study examined the nature and trend of price movement of garri in both markets (rural and urban), determined the annual rate of increase in the price of garri in the markets and tested if the differences in the price of garri in the rural and urban markets were significant. The prices used for the study were derived from the daily prices of garri collected by staff of the Edo State Agricultural Development Programme (EADP, 1990) from the various markets in Edo State, Nigeria. The data were analyzed using simple descriptive statistics (such as means, standard deviations, tables, graphs, etc) and the Student t-test. The study revealed that the price of garri for both markets were cheapest in the third quarter (months of July to September) and most expensive from the 4th to the 1st quarters (from December to around April). Generally, urban market prices for garri were higher than that of the rural markets for the period considered. The observed mean annual price increases (that is, growth rate) was 20.50 and 43.02% for rural and urban markets, respectively. When the differences in price of garri for both markets were tested on a four yearly basis, they were significantly different from each other implying a wide gap between the rural and urban markets occasioned by rising fuel prices over the years. The significant t-values obtained for the period on a four-yearly basis at the 5% level of significance were 1.342, 1.382, 2.250, and 2.321, respectively. Due to the disparity in prices between the rural and urban markets, it is recommended that government should direct her efforts towards stabilizing the price of fuel and create motorable roads in the rural areas where garri is produced in order to close up the wide price margin between the rural and urban markets. Key words: Garri, market prices, comparative analysis, food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".